Introduction: Machine learning and data visualisation with R.
Classification and prediction: Generalised linear model (GLM), linear discrimination analysis (LDA), k-nearest neighbors (KNN). R-based worked examples.
Resampling methods: Cross-validation (CV) and bootstrap. R-based worked examples.
Regression tree-based methods: Classification and regression trees (CART), bagging, random forests and boosting. R-based worked examples.
Support vector machines (SVM): Support vector classifier, regression SVM. R-based worked examples.
Machine Learning in Action:
(a) Biomedical and health data analysis;
(b) Bond default data analysis;
(c) Insurance data analysis;
(d) Financial data analysis;
(e) Other big data analysis.
This module appears in the following module collections.
Method of assessment
75% examination, 25% coursework
Bishop, C. M. (2006), Pattern Recognition and Machine Learning. Springer, New York
James, G, Witten, D., Hastie, T., Tibshirani, R. (2013) Introduction to Statistical Learning. Springer, New York.
Sweeting, P. (2011) Financial Enterprise Risk Management. Cambridge University Press. Cambridge.
See the library reading list for this module (Canterbury)
The intended subject specific learning outcomes. On successfully completing the module students will be able to:
1 demonstrate systematic understanding of the concepts involved in machine learning;
2 demonstrate the capability to solve complex problems using a high level of skill in calculation and manipulation of the material in the following areas: Supervised learning with R; data science for actuarial science, finance and other areas.
3 apply a range of concepts and principles in supervised learning in loosely defined contexts, showing good judgement in the selection and application of tools and techniques.
The intended generic learning outcomes. On successfully completing the module students will be able to:
1 work competently and independently, be aware of their own strengths and understand when help is needed;
2 demonstrate a high level of capability in developing and evaluating logical arguments;
3 communicate arguments confidently with the effective and accurate conveyance of conclusions;
4 manage their time and use their organisational skills to plan and implement efficient and effective modes of working;
5 solve problems relating to qualitative and quantitative information;
6 make effective use of information technology skills such as online resources (moodle);
7 communicate technical material effectively;
8 demonstrate an increased level of skill in numeracy and computation;
9 demonstrate the acquisition of the study skills needed for continuing professional development.
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Credit level 7. Undergraduate or postgraduate masters level module.
- ECTS credits are recognised throughout the EU and allow you to transfer credit easily from one university to another.
- The named convenor is the convenor for the current academic session.
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